US 12,093,883 B2
Automated delivery coordination and meeting scheduling for multiple-recipient orders received at a computer system
Jagadesh Ramaswamy Hulugundi, Bangalore (IN); Raghuveer Prasad Nagar, Kota (IN); Madhu Kumar Shivalingappa, Bangalore (IN); and Nalini Sampathkumar, Bangalore (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Mar. 10, 2022, as Appl. No. 17/654,279.
Prior Publication US 2023/0289713 A1, Sep. 14, 2023
Int. Cl. G06Q 10/0836 (2023.01); G06Q 30/0601 (2023.01)
CPC G06Q 10/0836 (2013.01) [G06Q 30/0635 (2013.01); G06Q 30/0641 (2013.01)] 17 Claims
OG exemplary drawing
 
8. A computer system for delivery coordination and meeting scheduling, comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is configured to perform a method comprising:
identifying an online business transaction performed by a user, the online business transaction including an order placed by the user, wherein the order includes a plurality of recipients;
generating a correlation pattern between a user profile of each of the plurality of recipients included in the order placed by the user and historical data features associated with the user profile of each of the plurality of recipients;
training a linear regression model using the generated correlation pattern to derive a recommendation including a synchronized delivery option and a synchronized meeting scheduling option for the plurality of recipients;
determining whether the order is associated with an event, the event including the plurality of recipients;
in response to the order being associated with the event, collecting information corresponding to the event;
based on the collected information corresponding to the event and the trained linear regression model, generating a first recommendation during checkout including the synchronized delivery option for the plurality of recipients;
based on the collected information corresponding to the event and the trained linear regression model, generating a second recommendation during the checkout including the synchronized meeting scheduling option for the plurality of recipients;
detecting the user modifying the order;
determining whether the modification is associated with the event;
based on the modification being associated with the event, collecting additional details associated with the event;
based on the collected additional details, updating the first recommendation; and
based on the collected additional details, updating the second recommendation.